API

class mixed_naive_bayes.MixedNB(categorical_features=None, max_categories=None, alpha=0.5, priors=None, var_smoothing=1e-09)

Naive Bayes classifier for Categorical and Gaussian models.

Note: When using categorical_features, MixedNB expects that for each feature, all possible classes are captured in the trining data X in the mixed_naive_bayes.mixed_naive_bayes.MixedNB.fit method. This is to ensure numerical stability.

Parameters

categorical_features (array-like shape (num_categorical_classes,) or) –
(default=None) ('all') – Columns which have categorical feature_distributions
max_categories (array-like, shape (num_categorical_classes,) (default=None)) – The maximum number of categories that can be found for each categorical feature. If none specified, they will be generated automatically.
alpha (non-negative float, optional (default=0)) – Additive (Laplace/Lidstone) smoothing parameter (0 for no smoothing). This is for features with categorical distribution.
priors (array-like, size (num_classes,), optional (default=None)) – Prior probabilities of the classes. If specified, the priors are not adjusted according to the data.
var_smoothing (float, optional (default=1e-9)) – Portion of the largest variance of all features that is added to variances for calculation stability.

priors

probability of each class.

Type: array, shape (num_classes,)

epsilon

absolute additive value to variances

Type: float

num_samples

number of training samples

Type: int

categorical_features

number of classes (number of layes of y)

Type: int

gaussian_features

the distribution for every feature and class

Type: array, shape (num_classes,)

categorical_posteriors

the distribution of the categorical features

Type: array

theta

the mean of the gaussian features

Type: array

sigma

the variance of the gaussian features

Type: array

References

https://scikit-learn.org/stable/modules/classes.html#module-sklearn.naive_bayes

Example

>>> from mixed_naive_bayes import MixedNB
>>> X = [[0, 0, 180, 75],
         [1, 1, 165, 61],
         [2, 1, 166, 60],
         [1, 1, 173, 68],
         [0, 2, 178, 71]]
>>> y = [0, 0, 1, 1, 0]
>>> clf = MixedNB(categorical_features=[0,1])
>>> clf.fit(X,y)
>>> clf.predict(X)